Efficacy of metformin targets on cardiometabolic health in the general population and non-diabetic individuals: a Mendelian randomization study

Summary Background Metformin shows beneficial effects on cardiometabolic health in diabetic individuals. However, the beneficial effects in the general population, especially in non-diabetic individuals are unclear. We aim to estimate the effects of perturbation of seven metformin targets on cardiometabolic health using Mendelian randomization (MR). Methods Genetic variants close to metformin-targeted genes associated with expression of the corresponding genes and glycated haemoglobin (HbA1c) level were used to proxy therapeutic effects of seven metformin-related drug targets. Eight cardiometabolic phenotypes under metformin trials were selected as outcomes (average N = 466,947). MR estimates representing the weighted average effects of the seven effects of metformin targets on the eight outcomes were generated. One-sample MR was applied to estimate the averaged and target-specific effects in 338,425 non-diabetic individuals in UK Biobank. Findings Genetically proxied averaged effects of five metformin targets, equivalent to a 0.62% reduction of HbA1c level, was associated with 37.8% lower risk of coronary artery disease (CAD) (odds ratio [OR] = 0.62, 95% confidence interval [CI] = 0.46–0.84), lower levels of body mass index (BMI) (β = −0.22, 95% CI = −0.35 to −0.09), systolic blood pressure (SBP) (β = −0.19, 95% CI = −0.28 to −0.09) and diastolic blood pressure (DBP) levels (β = −0.29, 95% CI = −0.39 to −0.19). One-sample MR suggested that the seven metformin targets showed averaged and target-specific beneficial effects on BMI, SBP and DBP in non-diabetic individuals. Interpretation This study showed that perturbation of seven metformin targets has beneficial effects on BMI and blood pressure in non-diabetic individuals. Clinical trials are needed to investigate whether similar effects can be achieved with metformin medications. Funding Funding information is provided in the Acknowledgements.


Introduction
Given the intersection and co-morbidity between type 2 diabetes (T2D) and cardiovascular disease, the efficacy and safety of anti-diabetic therapies on cardiometabolic health are of importance. 1 Metformin is the most widely used first-line anti-diabetic therapy that is taken by over 150 million people each year, 2 which shows good safety profiling on cardiovascular diseases.To date, clinical trials of metformin have provided evidence to support its beneficial effects on several cardiometabolic diseases in individuals with diabetes, including coronary death, major cardiovascular disease and body weight, 3 but its effect on blood pressure was unclear. 4Until now, few studies have investigated the generalizability of metformin effects on cardiometabolic diseases in the general population, where non-diabetic individuals with normal levels of glycated haemoglobin (HbA 1c ) levels are the majority. 5,6A study that estimates the average and target-specific causal effects of known metformin targets, and establishes the HbA 1c stratified effects of metformin targets will provide timely evidence to support the understanding of the HbA 1c lowering effects of metformin targets on cardiometabolic health in the general population, especially in non-diabetic individuals. 7endelian randomization (MR) is an epidemiologic approach that uses germline genetic variants as instruments to estimate the causal effect of a modifiable exposure on an outcome. 8MR has previously been used to evaluate drug efficacy 9 and inform potential safety concerns of approved drugs. 10The effects of two metformin targets, AMP-activated protein kinase (AMPK) and growth differentiation factor 15 (GDF15), on cardiovascular diseases have been studied. 11,12However, the general effect of metformin is influenced by multiple pharmacological targets, including, but not limited to AMPK, 13 mitochondrial complex I (MC1), 14

Research in context
Evidence before this study We searched PubMed, Embase and clinicaltrials.govdatabases from inception up to July 11, 2022 using the search terms: "metformin", "body mass index [BMI]", "coronary artery disease [CAD]", "systolic blood pressure [SBP]", "diastolic blood pressure [DBP]" "blood lipids", "Mendelian randomization" and "clinical trials", without language restrictions.117 MR and trial studies have investigated the role of metformin on cardiometabolic outcomes in individuals with diabetes, establishing metformin's effect on body mass index and heart disease, but studies examining metformin effects on blood pressure are under powered.Little has been done to estimate the generalizability of metformin effects on cardiometabolic outcomes-for instance, in the non-diabetic population.

Added value of this study
We confirmed the averaged beneficial effect of seven known metformin targets on reducing CAD risk, and reducing BMI, SBP and DBP levels.Mitochondrial complex I showed the strongest target-specific effects on CAD, BMI and DBP.The one-sample MR analysis in UK Biobank suggested that metformin targets also showed robust beneficial effects on cardiometabolic outcomes in non-diabetic individuals.

Implications of all the available evidence
Our study provides two pieces of evidence that may influence clinical decision making: (i) our findings support a beneficial effect of seven known metformin targets on cardiometabolic health in the general population and in non-diabetic individuals; (ii) Metformin targets showed protective effects on BMI, SBP and DBP control in non-diabetic individuals.Clinical trials are needed to investigate whether metformin medication (which may have additional targets than the seven studied here) can achieve similar reductions in cardiometabolic disease risk for individuals with high risk of developing diabetes, e.g.those with pre-diabetes. 15GDF15 16 and glucagon-like peptide-1 (GLP1), 17 fructose bisphosphatase-1 (FBP1) 18 and adenylyl cyclase (ADCY1). 19Ideally, we would like to reliably estimate the effect of metformin on cardiometabolic health, to highlight potential clinical uses, other than for diabetes treatment/management, and to inform design of clinical trials.To do this reliably, we would firstly need to know all metformin targets, and secondly be able to instrument them with genetic variants.These metformin targets are currently known and can be instrumented genetically.There are likely other targets that are yet unknown and/or cannot be instrumented.Thus, the current best estimate of the effects of metformin is to examine target specific effects to understand whether particular metformin targets are more important for a certain cardiometabolic disease, and this may also inform target-specific drug development.Novel molecular phenotypes, such as gene/ protein expression data, 20 and new methods, such as genetic colocalization, 21 have been proposed to select reliable genetic instruments for drug targets 12 such as metformin targets. 22he aim of this study was to estimate the averaged and target-specific effects of seven metformin targets on eight cardiometabolic phenotypes in the general population using two-sample MR.This study also attempted to investigate the effect of metformin targets on cardiometabolic health in non-diabetic individuals using one-sample MR.

Study design
Fig. 1 presents a diagram of our genetic instrument selection (Supplementary Tables S1-S6), data sources, and the main and follow-up analyses.All studies providing data to this analysis had the relevant institutional review board approval from each country and all participants provided informed consent.Summary results were obtained from genome-wide association studies (GWAS) of HbA 1c (N = 344,182) and eight cardiometabolic outcomes (average N = 466,947; Supplementary Table S7).
Supplementary Fig. S1 illustrates the causal questions being interrogated in this study: (i) the average and target-specific effects of metformin targets on the eight cardiometabolic outcomes in the general population; (ii) the effects of circulating HbA 1c on these outcomes (as a benchmark analysis); (iii) the effects of metformin targets on these outcomes in non-diabetic individuals.

Selection of genetic instruments for metformin targets and HbA 1c
We generated genetic instruments to proxy the lifelong effect of seven metformin targets (AMPK, MC1, MG3, GDF15, GLP1/GCG, FBP1 and ADCY1) and of changes in HbA 1c levels.To select instruments for perturbation of metformin targets, we applied a conventional instrument selection process that has been used in previous drug target MR. 12,22In summary, this process selected genetic variants that: (i) associated with both expression levels of corresponding genes (P < 0.01; N ≤ 31,684, data from GTEX, 20 eQTLGen 23 and Zheng et al. 24 ) in the cisacting regions (500 kb window from the centinal variant) and HbA 1c levels (P < 0.05; N = 344,182, data from UK Biobank); (ii) showed evidence of genetic colocalization between the expression levels and HbA 1c levels within the corresponding genomic regions (colocalization probability >0.7); (iii) passed a stringent linkage disequilibrium (LD; which is the pairwise squared correlation between nearby variants) r 2 threshold of 0.001, which implies independent instruments to proxy perturbation of metformin targets.(iv) have minor allele frequency over 1%.

Study outcomes
We selected cardiometabolic outcomes that are currently undergoing clinical trials for metformin use as outcomes for the MR analyses.We searched for the word 'metformin' in the CHEMBL 27 S7).Type 2 diabetes was considered as a validation outcome.The genetic associations for these eight cardiometabolic outcomes and T2D were extracted from GWAS studies with an average of 466,947 samples per outcome, which were among the largest available studies for these outcomes to date. 28We noticed that the SBP and DBP GWAS included BMI as a covariate in the regression model. 29To avoid the issue of different covariates in the exposure and outcome, 30 we used UK Biobank SBP and DBP GWAS (not adjusted for BMI in the model) as a validation analysis.

Statistical analyses
Germline genetic variants used to proxy the perturbation of each of the metformin targets were matched to the eight cardiometabolic outcome datasets by harmonizing effects to the same effect allele and excluding palindromic variants with minor allele frequencies over 0.4.If an instrument was not available in the outcome dataset, a genetic variant in high LD (r 2 > 0.8) with the instrument was selected as a proxy instrument.For the tested metformin targets, MR estimates were first generated per individual variant using the Wald ratio and standard errors were estimated using the delta method.A random-effects inverse-variance weighted meta-analysis was then used to combine variant-level Wald ratio estimates into a weighted-average effect estimate representing the overall HbA 1c lowering effect via metformin targets.All MR estimates (odds ratios [ORs] for analyses of binary outcomes and beta coefficients for analyses of continuous outcomes) were scaled to represent an SD unit of HbA 1c lowering.This reflects the equivalent of a 0.62% reduction in HbA 1c .
In the main MR analysis, the average effects of the HbA 1c lowering effect of five metformin targets (proxied by all 34 metformin instruments together) on the eight cardiometabolic outcomes were estimated.For targetspecific analysis, the specific effects of each of the metformin targets (i.e.seven targets as seven separate exposures) on the eight cardiometabolic outcomes were estimated.As a benchmark, the effects of circulating HbA 1c levels on the eight cardiometabolic outcomes were estimated to understand the influence of glucose on cardiometabolic outcomes.In this study, negative betas refer to a lower value of the outcome trait caused by genetically proxied lower HbA 1c levels, as this represents the potential effect of metformin.Likewise, for binary outcomes, an odds ratio below 1 refers to the protective effect of genetically proxied lower HbA 1c levels.

One-sample MR and triangulation analyses
For MR estimates with evidence to support the HbA 1c lowering effect of metformin targets on a cardiometabolic outcome (Bonferroni-corrected threshold P < 0.006), we conducted extensive follow-up analyses of these outcomes in non-diabetic participants from UK Biobank (identified by excluding individuals with ICD-10 codes E10, E11, E12, E13, E14 and O24).These outcomes included BMI (UK Biobank ID: 21,001, unit kg/m 2 ), SBP (UKBB ID: 4080; unit mmHg) and DBP (UKBB ID: 4079; unit mmHg).First, we conducted a one-sample MR to estimate the effects of perturbation of metformin targets on BMI, SBP and DBP in 338,425 non-diabetic participants.The genetic scores of the metformin targets are listed in Supplementary Table S6a (Supplementary Note 2).The effect of metformin targets on HbA 1c was estimated as a validation of the metformin instruments.The weighted average effect of metformin targets (34 instruments) as well as the target-specific effects (each target as an independent exposure) on BMI, SBP and DBP were estimated.CAD passed the linear MR threshold but was not considered as an outcome in these analyses due to the limited number of incident cases in the UK Biobank (N cases = 8891).Second, we triangulated genetic evidence from MR, pharmacoepidemiologic and trial evidence from the literature to validate the metformin effects on BMI, 31 SBP and DBP 32 (Supplementary Note 3).
In addition, since one-sample MR may overfit the data, we applied recently proposed two-sample MR approaches using single large-scale cohort, 33 which may provide more accurate causal estimates.The 34 metformin drug target instruments were derived from separate dataset to avoid issue of winner's curse, with genetic associations information of HbA 1c , BMI, SBP and DBP derived from the same 338,425 non-diabetic participants from UK Biobank.The inverse variance weighted, weighted median, weighted mode and Wald ratio two-sample MR methods were applied for this analysis.

Validation of Mendelian randomization assumptions
In this study, we report findings according to the STROBE-MR (Strengthening the Reporting of Mendelian Randomization Studies) guidelines. 34MR has three key assumptions (Supplementary Fig. S2): (i) the germline genetic instruments used to proxy the drug targets are robustly associated with the exposure ("relevance"); (ii) the instruments are not associated with common causes (confounders) of the instrumentoutcome relationship ("exchangeability"); (iii) the instruments are only associated with the outcome through the exposure under study ("exclusion restriction").These MR assumptions were tested using a set of sensitivity analyses, although the "exchangeability" and "exclusion restriction" assumptions cannot be shown to hold directly.
The relevance assumption was validated by estimating the strength of the genetic predictors for each metformin target using the proportion of variance in each exposure explained by the predictor (R 2 ) and Fstatistics using the approximate approach.F-statistics above 10 are indicative of evidence against weak instrument bias.The exclusion restriction assumption was tested using a set of sensitivity analyses which relied on different assumptions.First, HbA 1c is a long-term blood glucose biomarker that also associated with red blood cell traits.Therefore, red blood cell traits may create a potential pleiotropic pathway linking metformin targets with cardiometabolic outcomes.To avoid this issue, we conducted a multivariable MR of metformin targets plus blood cell counts on cardiometabolic outcomes (Supplementary Table S6c).In contrast to univariable MR (which estimates a total causal effect), multivariable MR estimates the direct effect of an exposure of interest, by accounting for the indirect effect via secondary exposures.Second, we conducted a set of pleiotropy robust methods, including phenome-wide association study of instruments using PhenoScanner 35 (Supplementary Table S6d), weighted median analysis, and simple and weighted mode estimator analyses.The heterogeneity between instruments was estimated using Cochran's Q test.A single variant MR comparison was carried out to examine whether MR estimates were driven by a single influential variant in drug target proxies.All these sensitivity methods were conducted using functions implemented in the TwoSampleMR package. 36or all analyses, the MR effect estimates were odds ratio (OR) with 95% confidence interval (CI) and relevant P values.A conservative Bonferroni-corrected threshold was used to account for multiple testing.In total, one exposure (averaged HbA 1c lowering effect of metformin targets) was tested against eight cardiometabolic outcomes (0.05/8 statistical tests; P value threshold = 0.006).

Ethics
No additional ethical approval was required for the present study, since all analyses were based on publicly available statistics or individual level data from UK Biobank under application (17,295).The included GWAS studies all received informed consent from the study participants and have been approved by pertinent local ethical review boards.

Role of funders
The funders were not involved in any activities of the current study, including study design, data collection, data analyses, interpretation, or writing of report.

Strength of the genetic predictors of the perturbation of metformin targets
The instrument strength analysis suggested that instruments for the MC1, AMPK, GCG/GLP1, MG3, FBP1 and ADCY1 targets were deemed strong (F-statistics >10; Supplementary Table S6a).The instrument for GCG had an F-statistic = 1.40, which was excluded in the target-specific MR analysis with a caveat for potential weak instrument bias.

Effects of perturbation of metformin targets on cardiometabolic outcomes
Genetically proxied perturbation of metformin targets, equal to 1 standard deviation unit (SD, 0.62%) lowering of HbA 1c , reduced CAD risk by 37.8% (OR = 0.62, 95% CI = 0.46-0.84,P = 0.002; Supplementary Table S8a S8b, Fig. 2).Metformin targets showed little evidence of association with lipid phenotypes and atrial fibrillation (Supplementary Table S8).Our study also validated the overall effect of perturbation of metformin targets on reducing T2D risk in the general population (OR = 0.57, 95% CI = 0.35-0.92,P = 0.02; Supplementary Table S8a).The SBP and DBP data used in the main analysis adjusted for BMI in the original GWAS. 29A sensitivity analysis using SBP and DBP unadjusted for BMI still supports the potential role of metformin targets on these two blood pressure traits (Supplementary Table S8b).To minimize the influence of a target proxied by a single instrument, we conducted a sensitivity analysis excluding GDF15, GCG/GLP1 and MG3 instruments and only kept MC1 and AMPK instruments.This analysis still supports the effect of the two targets on reduced CAD risk and decreased BMI, SBP and DBP levels (Supplementary Table S8c).
In this study, we proxied the effect of metformin targets by using their HbA 1c lowering effect.This reflects a longer-term effect of blood glucose, but HbA 1c is also influenced by red blood cell traits.To control the impact of red blood cell traits on the MR estimates, we conducted a multivariable MR of metformin targets on cardiovascular disease (CVD) outcomes adjusted for red blood cell counts.The results still supported the putative causal roles of metformin targets on CAD, BMI, SBP and DBP (Supplementary Table S8d).
As a sensitivity analysis, we included two more targets of metformin, FBP1 and ADCY1, into the MR model (34 instruments in total) and estimated the effects of seven targets on the CVD outcomes.This analysis suggested that the effects of metformin targets was associated with reduced CAD and T2D risk, and decreased DBP, LDL-C and BMI levels (Supplementary Table S8e).These findings were similar with the general effect estimated using five metformin targets.The exception was LDL-C, in which LDL-C showed marginal evidence of an effect using five targets, but more robust evidence using seven targets.
For our top findings, the five MR sensitivity analyses showed little evidence to support violation of the exclusion restriction MR assumption (Supplementary Tables S8-S10).

One-sample MR and triangulation analyses of top findings
Table 1 presented the one-sample MR results of perturbation of metformin targets on BMI, SBP and DBP in non-diabetic participants in UK Biobank.The genetically-predicted metformin targets showed strong effects on HbA 1c levels (except GCG), which validated the reliability and power of the instruments.The onesample MR analysis suggested that the weighted average HbA 1c lowering effect of seven metformin targets causes lower levels of BMI (β = −0.17,95% CI = −0.28 to −0.07), SBP (β = −0.61,95% CI = −1.07 to −0.14) and DBP (β = −0.90,95% CI = −1.17 to −0.63) in non-diabetic individuals (Table 1).The target-specific one-sample MR analysis further indicated that the genetically-predicted HbA 1c lowering effects of MC1, AMPK and ADCY1 cause lower levels of BMI; HbA 1c lowering effects of MC1 and AMPK cause lower levels of SBP; while HbA 1c lowering effects of MC1 cause lower levels of DBP (Table 1).This target-specific analysis highlights the importance of MC1 and AMPK pathway on cardiometabolic health.The two-sample MR results using data from single cohort suggested consistent causal relationships between metformin targets and BMI, SBP and DBP.One additional finding was the HbA 1c lowering effect of FBP1 on DBP (β = −3.69,95% CI = −6.21 to −1.22; Table 1).
We further triangulated the existing clinical trial evidence from the literature with the genetic evidence we obtained from the MR analysis (Supplementary Table S11).For BMI, SBP and DBP, evidence from the meta-analysis of trials and our MR analyses both demonstrated metformin effects consistent with a reduction in BMI and DBP (Fig. 3).

Discussion
In this study, we estimated the effects of seven known metformin targets on eight cardiometabolic outcomes and have shown that the HbA 1c lowering effect of metformin targets leads to an improvement in a wide range of cardiometabolic conditions, including CAD, BMI and blood pressure, in the general population.To date, meta-analyses of clinical trials have suggested a beneficial effect of metformin on reducing cardiometabolic disease risk, but the decision to approve metformin for treating these conditions is still under investigation by the U.S. Food and Drug Administration (FDA).The one-sample MR further showed the effect of metformin targets on reducing BMI, SBP and DBP levels in non-diabetic individuals.This study therefore provides evidence to support the prioritization of metformin as a drug to improve cardiometabolic health in the general and non-diabetic population.
Previous epidemiological and MR studies have suggested that metformin and its target AMPK may protect against cardiovascular disease. 6,11The UK Prospective Diabetes trial (UKPDS) demonstrated that glucose lowering by metformin compared with only diet control reduced all-cause mortality and myocardial infarction in newly diagnosed T2D overweight patients. 37However, an existing meta-analysis of clinical trials suggested weak evidence to support the role of metformin on reducing risk of cardiovascular disease among individuals with diabetes. 38A recent MR study suggested the role of metformin activator, AMPK, on CAD. 11Our study provides two additional pieces of evidence: (1) the average effect of seven metformin targets on CAD in the general population; (2) the target specific effect of mitochondrial complex I on reducing CAD risk.
Existing epidemiological and preclinical studies have suggested that metformin has favourable effects in terms of reducing body weight. 39However, existing large-scale trials showed modest and inconsistent effects of metformin on weight loss. 40Due to this existing evidence, the US FDA has not approved metformin as a stand-alone weight loss agent.The current clinical guidelines do not recommend metformin as a monotherapy for obese patients without diabetes.However, off-label usage of metformin as an anti-obesity agent for non-diabetic individuals is relatively common in clinical practice.Our results using genetics suggested a weighted averaged causal effect of perturbation of metformin targets on reducing BMI levels, in the general and non-diabetic populations.Whether metformin could be suggested as a weight-loss agent to the general population, especially to those non-diabetic individuals at high risk of getting diabetes, is worthy of clinical investigation in future metformin trials.For blood pressure, a few clinical trials among diabetic patients have shown little evidence of an effect of metformin on BP levels. 4,41A more recent meta-analysis of 28 trials consisting of 4113 non-diabetic participants suggested that metformin could effectively lower SBP in non-diabetic patients. 42,43In vivo studies have suggested a few potential mechanisms for metformin influencing blood pressure, which include some non-glycemic mechanisms such as reduction of intracytoplasmic calcium and improvement of endothelial function. 44Our study suggested that perturbation of metformin targets may have a general causal effect on reducing SBP and DBP levels in the general and non-diabetic populations.Future trials are needed to investigate the effect of metformin on blood pressure in non-diabetic individuals with dysglycemia and/or pre-diabetes symptoms.
The effects of HbA 1c on CVD outcomes were discussed in this study.For CAD, previous MR evidence and our results support a causal role of HbA 1c on CAD. 45For blood pressure, some studies support a causal role of blood pressure on diabetes, 46,47 whilst our study suggests a causal role of HbA 1c on SBP.For body weight, BMI is a clear causal risk factor for a set of diseases including T2D. 48However, the effect of blood glucose on body weight is less clear and potentially non-causal.Collectively, the causal effect of blood glucose on blood pressure and body weight needs investigation.
Our study has several strengths.First, our study validated the beneficial effects of several known metformin targets on heart disease, body weight and blood pressure.These findings may open opportunities to further investigate the target-specific effects of metformin.Second, our study aimed to understand the role of metformin targets on cardiometabolic health in nondiabetic individuals.It is known that the efficacy of metformin on HbA 1c reduction is not equal in diabetic and non-diabetic patients (1.14% vs 0.13%), with glucose-stimulated insulin secretion of metformin proposed as one of the underlying mechanisms. 49Our MR results suggest that metformin's efficacy on cardiometabolic conditions, especially on blood pressure and body weight, is likely to be true, even in those without diabetes.This may provide timely evidence to support some ongoing trials such as the VA-IMPACT trial, which aims to evaluate cardiovascular outcomes in patients with pre-diabetes and established CVD treated with metformin versus placebo (NCT02915198).

Caveats and limitations
Our study also has limitations.First, by definition, MR estimates the effects of perturbation of metformin targets on the outcomes rather than the direct effect of metformin use.In this study, we systematically estimated the causal roles of known metformin targets on eight cardiometabolic outcomes.However, combining effects from different targets into a single MR does not necessarily reflect the true biological action of the drug because not all targets are likely to have been captured here, and the unmeasured targets could potentially change the results.In addition, our meta-analysis model assumes no strong interaction between the five metformin targets, which may not always be true.As a validation analysis, a factorial MR of AMPK and MC1 targets in the UK Biobank was conducted but underpowered (results not shown).Therefore, we did not systematically apply the interaction model in this study.Second, our MR estimates of the effect of perturbation of metformin targets were weighted by the precision of each target, and then scaled to represent reductions in HbA 1c levels rather than the direct effect of the drug target.This assumes that the effect of perturbation of metformin targets is proportional to HbA 1c lowering.Third, this study used HbA 1c data as exposure, which brings good statistical power.But the downside is that HbA 1c is also influenced by blood cell phenotypes (e.g.red blood cell counts).Our multivariable MR confirmed that the effects of metformin targets on cardiometabolic outcomes were still robust even after controlling for the influence of blood cell phenotypes.Fourth, although the MR and HbA 1c MR results implied the possibility of glycemic dependent and independent effect of metformin on cardiometabolic outcomes, we were not able to formally investigate this using multivariable MR since the metformin target effects and HbA 1c were both proxied by circulating HbA 1c levels from the same UK Biobank study.The non-glycemic causal mechanisms of metformin on cardiometabolic health are therefore worthy of further investigation.Fifth, GDF15 is a target of interest to endocrinologists.A recent study showed that the effect of metformin on body weight control was mediated by circulating levels of GDF15. 503][54][55] In our study, the strength of GDF15 instruments was insufficient to provide robust evidence of a causal effect, although we still observed some evidence of a targetspecific effect of GDF15 on CAD.Fifth, the onesample MR was conducted in non-diabetic individuals, which represented as selection of samples and may increase the possibility of collider bias (also known as selection bias). 56,57A collider (e.g.diagnosis of diabetes) is a shared causal consequence of the exposure (e.g.HbA 1c , a marker of diagnosis of diabetes) and outcome (e.g.BMI, as thinner people are less likely to be diagnosed with diabetes), which may induce spurious associations between the metformin targets and cardiovascular outcomes.However, the two-sample MR in the general population yields similar MR results, and is less likely to be influenced by this collider bias issue, suggesting our results were likely to be valid.Finally, the one sample MR estimate using two-stage least squares method is more prone to data overfitting than twosample MR.In this study, we were inspired by a recent study that used two-sample MR methods in a single cohort. 33The results of the one-sample and twosample MR estimates in non-diabetic individuals in the UK Biobank were generally similar.This implies that although the one-sample MR results were likely to be overfitted, the causal relationships between metformin targets and cardiometabolic phenotypes in nondiabetic individuals are likely to be true.
From a clinical perspective, our study suggests that these metformin targets are likely to have a causal role in reducing CVD burden in the general population and in non-diabetic individuals.This finding provides evidence to consider metformin as a potential intervention target for CVD prevention in non-diabetic individuals.This evidence supports further investigation of the efficacy of metformin targets with glucose levels, especially in those with pre-diabetes symptoms.

Conclusions
Our results represent a comprehensive assessment of genetically proxied effects of perturbation of metformin targets on eight cardiometabolic outcomes.Our results provide evidence to support the general and targetspecific effects of metformin on benefiting cardiometabolic health.The different effects of metformin use on cardiometabolic outcomes in different subgroups of participants need to be evaluated in future clinical trials.The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.All authors read and approved the final version of the manuscript.

Data sharing statement
The GWAS summary data used in this study are publicly available via the IEU OpenGWAS database (https://gwas.mrcieu.ac.uk/).Declaration of interests T.R.G, J.Z. and G.D.S have received research funding from various pharmaceutical companies to support the application of Mendelian randomization to drug target prioritization.G.D.S. reports Scientific

Fig. 1 :
Fig. 1: Genetic instrument selection, data sources, and analysis strategy in a study of the lifelong effect of genetically proxied perturbation of metformin targets on cardiometabolic phenotypes.

Fig. 2 :
Fig. 2: Effects of perturbation of metformin targets and lowering of circulating HbA 1c levels on (a) coronary artery disease, (b) body mass index, (c) systolic blood pressure and (d) diastolic blood pressure in the general population.Both effect of perturbation of metformin and circulating HbA 1c effect were scaled to the same unit of 0.62% lowering of HbA 1c (which refers to 1 SD unit of HbA 1c levels) to allow direct comparison of the MR estimates.

Fig. 3 :
Fig. 3: Triangulation of clinical trial/observational and genetic evidence for perturbation of metformin targets on body mass index and diastolic blood pressure.(a) The effect of perturbation of metformin targets on body mass index; (b) the effect of perturbation of metformin targets on systolic blood pressure; (c) the effect of perturbation of metformin targets on diastolic blood pressure.The clinical trial (RCT) or observational (observed) effect size and genetic (MR) effect size were re-scaled to the same unit, so these pieces of evidence are comparable.

Table 1 :
Effect of target specific HbA1c lowering effects of metformin targets on body mass index, systolic blood pressure and diastolic blood pressure in UK Biobank participants without type 2 diabetes.